Personas versus clones for player decision modelling

Abstract

The current paper investigates how to model human play styles. Building on decision and persona theory we evolve game playing agents representing human decision making styles. Two methods are developed, applied, and compared: procedural personas, based on utilities designed with expert knowledge, and clones, trained to reproduce play traces. Additionally, two metrics for comparing agent and human decision making styles are proposed and compared. Results indicate that personas evolved from designer intuitions can capture human decision making styles equally well as clones evolved from human play traces.